Search Results (14,519)

Photocatalytic overall water splitting (PWS) offers a green, economical, and sustainable pathway for hydrogen production. However, the efficiency is still hindered by severe charge recombination in catalysts, high energy barriers for water oxidation, and sluggish reaction kinetics. Therefore, it is crucial to address these challenges by enhancing charge separation efficiency, accelerating reaction kinetics, and lowering PWS energy barriers. In this work, we constructed donor–acceptor covalent triazine-based organic frameworks (CTFs), such as CTF-BP, CTF-DBT, and CTF-DBTS, using biphenyl (BP), benzothiophene (DBT), and benzothiophene sulfone (DBTS) as basic units, respectively. DFT calculations revealed that all three CTFs exhibit comparable bandgaps with strong visible-light absorption. Notably, strong dipole moments between donor and acceptor units were observed within these frameworks, effectively promoting in-plane charge separation. DBT and DBTS derivatives generated stronger dipole moments compared to biphenyl. Furthermore, hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) pathway analyses demonstrated that CTF-DBTS substantially reduces energy barriers for both half-reactions relative to CTF-DBT and CTF-BP, exhibiting the most promising potential for PWS. This work provides a reference for the application of DBTS-incorporated COFs in PWS systems. Full article

A comprehensive understanding of aerodynamic instabilities, such as flutter, non-synchronous vibration (NSV), rotating stall, and forced response, is crucial for the safe and efficient operation of turbomachinery, particularly fans and compressors. These instabilities impose significant limitations on the operating envelope, necessitating precise monitoring and accurate quantification of vibration amplitudes during experimental investigations. This study addresses the challenge of measuring these amplitudes by comparing multiple measurement systems applied to the open-test case of the ultra-high bypass ratio (UHBR) fan ECL5. During part-speed operation, the fan exhibited a complex aeromechanical phenomenon, where an initial NSV of the second blade eigenmode near peak pressure transitioned to a dominant first-mode vibration. This mode shift was accompanied by substantial variations in blade vibration patterns, as evidenced by strain gauge data and unsteady wall pressure measurements. These operating conditions provided an optimal test environment for evaluating measurement systems. A comprehensive and redundant experimental setup was employed, comprising telemetry-based strain gauges, capacitive tip timing sensors, and a high-speed camera, to capture detailed aeroelastic behaviour. This paper presents a comparative analysis of these measurement systems, emphasizing their ability to capture high-resolution, accurate data in aeroelastic experiments. The results highlight the critical role of rigorous calibration procedures and the complementary use of multiple measurement technologies in advancing the understanding of turbomachinery instabilities. The insights derived from this investigation shed light on a complex evolution of instability mechanisms and offer valuable recommendations for future experimental studies. The open-test case has been made accessible to the research community, and the presented data can be used directly to validate coupled aeroelastic simulations under challenging operating conditions, including non-linear blade deflections. Full article

Considering the high stability of water-in-oil (W/O) emulsions, contamination from emulsified pollutants poses a long-term risk to the environment. In this study, hybrid membranes composed of wheat gluten amyloid fibrils (WGAFs) and zein nanoparticles (ZNPs) were prepared and used as a separator to remove emulsified W/O droplets from the oily phase. ZNPs and WGAFs were synthesized through antisolvent method and fibrillation process. Next, a ZNP-functionalized wheat gluten AF/methyl cellulose (ZNP-WGAF/MC) hybrid membrane was fabricated, and its properties were investigated via various analytical techniques. Lastly, the separation efficiency of the ZNP-WGAF/MC hybrid membrane for various W/O emulsions was assessed using microscopy and light scattering. The formation of ZNPs or WGAFs was first verified via spectroscopic and microscopic methods. Our results indicated that the ZNP-WGAF/MC hybrid membranes were synthesized via chemical crosslinking coupled with the casting method. Furthermore, the incorporation of either WGAFs or ZNPs was found to improve the thermal stability and surface hydrophobicity of membranes. Finally, the separation efficiency of the ZNP-WGAF/MC hybrid membranes for various W/O emulsions was determined to be ~87–99%. This research demonstrates the potential of harnessing three-dimensional membranes composed of plant protein-based fibrils and nanoparticles to separate emulsified W/O mixtures. Full article

Background: Redox-responsive prodrug nanoassemblies (NAs) have been extensively utilized in precise cancer therapy. But there is no research shedding light on the impacts of the π–π stacking interactions on the self-assembly capacity of redox-responsive prodrugs and the in vivo delivery fate of NAs. Methods: Three structurally engineered doxorubicin (DOX) prodrugs (FAD, FBD, and FGD) were developed through α-, β-, and γ-positioned disulfide linkages with π-conjugated Fmoc moieties. The NAs were comprehensively characterized for their self-assembly kinetics, redox-responsive drug release profiles, and physicochemical stability. Biological evaluations included cellular uptake efficiency, in vivo pharmacokinetics, and antitumor efficacy in tumor-bearing mouse models. Results: Systematic characterization revealed that π-conjugated disulfide bond positioning dictates prodrug self-assembly and inversely regulates reductive drug release relative to carbon spacer length. The FBD NAs demonstrated optimal redox-responsive release kinetics while maintaining minimal systemic toxicity, achieving 101.7-fold greater tumor accumulation (AUC) than DiR Sol controls. In 4T1 tumor-bearing models, FBD NAs displayed potent antitumor efficacy, yielding a final mean tumor volume of 518.06 ± 54.76 mm³ that was statistically significantly smaller than all comparator groups (p < 0.001 by ANOVA at a 99% confidence interval). Conclusion: These findings demonstrate that strategic incorporation of redox-sensitive disulfide bonds with different π–π stacking interactions in the prodrug structure effectively optimizes the delivery-release balance of DOX in vivo, ensuring both potent antitumor efficacy and reduced systemic toxicity. Full article

Water contamination by emerging pollutants poses a significant environmental challenge, demanding innovative treatment technologies beyond conventional methods. Advanced oxidation processes (AOPs) utilizing niobium-based catalysts, particularly niobium oxide (Nb₂O₅) and its modified forms, are prominent due to their high chemical stability, effective reactive oxygen species (ROS) generation, and versatility. This review systematically examines recent advancements in Nb₂O₅-based catalysts across various AOPs, including heterogeneous photocatalysis, electrocatalysis, and Fenton-like reactions, highlighting their mechanisms, material modifications, and performance. Following PRISMA and InOrdinatio guidelines, 381 papers were selected for this synthesis. The main findings indicate that niobium incorporation enhances pollutant degradation by extending light absorption, reducing electron–hole recombination, and increasing ROS generation. Structural modifications such as crystalline phase tuning, defect engineering, and the formation of heterostructures further amplify catalytic efficiency and stability. These catalysts demonstrate considerable potential for water treatment, effectively degrading a broad range of persistent contaminants such as dyes, pharmaceuticals, pesticides, and personal care products. This review underscores the environmental benefits and practical relevance of Nb₂O₅-based systems, identifying critical areas for future research to advance sustainable water remediation technologies. Full article

The g-C₃N₄/ZnO nanocomposite materials were applied to degrade methylene blue (MB). The samples were characterized and evaluated to study the adsorption and photocatalytic degradation under visible light. The g-C₃N₄ was incorporated at percentages of 5%, 10%, 20%, and 40% relative to the ZnO weight. These composite materials were prepared using a solvothermal microwave technique. The structural, textural, morphological, and optical properties were investigated using XRD, FTIR, SEM, EDS, STEM, BET, UV-Vis, and XPS techniques. The XRD patterns of the samples showed the coexistence of crystalline phases of g-C₃N₄ and ZnO, while images and elemental composition analysis confirmed the formation of nanocomposite samples. The UV-Vis spectrum revealed a redshift in the absorption edge of the nanocomposites, indicating improved light-harvesting capability. The synthesized material g-C₃N₄/ZnO (20/80), with a surface area of 25 m²/g, exhibited higher photocatalytic performance, achieving 85% degradation of MB after 100 min under visible light, which corresponds to nearly three times the degradation efficiency of commercial P25-TiO₂ (31%) under the same conditions. The reusability and stability tests were conducted up to the fifth cycle, and this material showed 77% degradation, indicating good stability. This nanocomposite material has good potential as a photocatalyst for solar-driven MB. Full article

Despite intensive worldwide research efforts and multiple available therapeutic schemes for cancer treatment, cancer still remains a challenge, rendering the need for the discovery of new therapeutic approaches imperative. Photodynamic therapy (PDT) is a novel, non-invasive anti-cancer treatment that relies on the generation of reactive oxygen species (ROS) that are cytotoxic to cancer cells. ROS are generated by the interaction between a photosensitizer (PS) drug, a light source (primarily a laser), and oxygen. Although PDT offers the advantage of using non-ionizing radiation and bears great therapeutic potential, it has not yet been widely adopted in clinical practice. This review summarizes the new developments in the use of PDT in combination with chemotherapy, immunotherapy, and radiotherapy, giving emphasis to the combination of PDT with a novel type of therapy that also takes into account the tumor microenvironment (TME) to enhance treatment efficacy. TME-targeting therapies include strategies like hypoxia modulation, vascular normalization, and immune cell reprogramming. Interestingly, when combined with PDT, these therapies can improve therapeutic outcomes while reducing side effects, and nanoparticle-based delivery systems have demonstrated the potential to enhance PDT selectivity and efficiency. This review highlights PDT’s enormous potential in treating various cancer types and underscores the need for continued exploration of combination therapies to maximize its clinical impact. Full article

Blockchain and smart contracts have transformed industries by automating complex processes and transactions. However, this innovation has introduced significant security concerns, potentially leading to loss of financial assets and data integrity. The focus of this research is to address these challenges by developing a tool that can enable developers and testers to detect vulnerabilities in smart contracts in an efficient and reliable way. The research contributions include an analysis of existing literature on smart contract security, along with the design and implementation of a lightweight vulnerability detection tool called LightCross. This tool runs two well-known detectors, Slither and Mythril, to analyse smart contracts. Experimental analysis was conducted using the SmartBugs curated dataset, which contains 143 vulnerable smart contracts with a total of 206 vulnerabilities. The results showed that LightCross achieves the same detection rate as SmartBugs when using the same backend detectors (Slither and Mythril) while eliminating SmartBugs’ need for a separate Docker container for each detector. Mythril detects 53% and Slither 48% of the vulnerabilities in the SmartBugs curated dataset. Furthermore, an assessment of the execution time across various vulnerability categories revealed that LightCross performs comparably to SmartBugs when using the Mythril detector, while LightCross is significantly faster when using the Slither detector. Finally, to enhance user-friendliness and relevance, LightCross presents the verification results based on OpenSCV, a state-of-the-art academic classification of smart contract vulnerabilities, aligned with the industry-standard CWE and offering improvements over the unmaintained SWC taxonomy. Full article

This review comprehensively presents the development of energy-efficient pneumatic propulsion systems (PPSs) in road vehicle applications, which are classified as green vehicles. The advantages and disadvantages of PPSs were presented, and PPSs were compared with combustion propulsion systems (CPSs) and electric propulsion systems (EPSs), as well as their power-to-weight ratios (PWRs), energy densities, and CO₂ emissions. The review of compressed air vehicles (CAVs) focuses on their historical development and future prospects. This review discusses the use of PPSs with compressed air engines (CAEs) as an alternative propulsion system in green vehicles, providing a simple, energy-saving, and environmentally friendly solution. This review also discusses hybrid air propulsion, which, when combined with internal combustion engines (ICEs) or electric motors (EMs), offers innovative energy-efficient propulsion systems that are more economical than conventional hybrid propulsion systems. This review focuses on recent advances in lightweight air vehicles that improve vehicle handling, increase efficiency, and reduce propulsion energy consumption. Discussion of the study results concerns the use of PPSs in a three-wheeled rehabilitation tricycle (RTB). A comprehensive computation model of the RTB was presented, and the key performance parameters crucial to its operation were analyzed. The results of the RTB simulation were verified through field tests. Full article

Resurrection plants such as Ramonda serbica and Ramonda nathaliae are gaining scientific attention due to their exceptional ability to withstand extreme drought and cold. This study is the first to evaluate the changes in photosynthetic activity, antioxidant defense, and the role of protective proteins during the early hours of recovery of these species after freezing-induced desiccation. Specimens collected from natural habitats where temperatures dropped below −10 °C were rehydrated under controlled conditions, and measurements were taken at multiple time points from 1 h up to 7 days after recovery. Both species demonstrated a gradual increase in photosynthesis, with the CO₂ assimilation rate significantly improving after 24 h and reaching full restoration by day 7. This recovery aligned with increases in relative water content and stomatal conductance. Photosystem II efficiency was fully restored within 72 h. Notably, R. nathaliae exhibited higher thermal dissipation during stress than R. serbica. Antioxidant activity peaked between 1 and 3 h of rehydration and returned to baseline by day 7. Additionally, early rehydration stages triggered the accumulation of stress-related proteins such as dehydrins, early light-inducible proteins, small heat shock proteins, and fatty acid amide hydrolase. These results provide valuable insights into the desiccation–rehydration mechanisms of Ramonda species, demonstrating that they fully recover physiological functions within seven days and highlighting species-specific stress responses during early rehydration. Full article

Efficient navigation of Automated Guided Vehicles (AGVs) in dynamic warehouse environments requires real-time and accurate path segmentation algorithms. However, traditional semantic segmentation models suffer from excessive parameters and high computational costs, limiting their deployment on resource-constrained embedded platforms. A lightweight image segmentation algorithm is proposed, built on an improved ESPNet-C architecture, combining Spatial Group-wise Enhance (SGE) and Efficient Channel Attention (ECA) with a dual-branch upsampling decoder. On our custom warehouse dataset, the model attains 90.5% Miou with 0.425 M parameters and runs at ~160 FPS, reducing parameters by ×116–×136 and computational costs by 70–92% in comparison with DeepLabV3+. The proposed model improves boundary coherence by 22% under uneven lighting and achieves 90.2% Miou on the public BDD100K benchmark, demonstrating strong generalization beyond warehouse data. These results highlight its suitability as a real-time visual perception module for AGV navigation in resource-constrained environments and offer practical guidance for designing lightweight semantic segmentation models for embedded applications. Full article

Element doping and functional group modification engineering serve as efficient approaches that contribute to the improvement of the functional efficiency in graphitic carbon nitride (CN) materials. A CN photocatalyst co-modified with sulfur (S) and cyano moieties was prepared through thermal condensation polymerization. The introduced S species modulated the band structure, increased charge carrier mobility, and significantly promoted charge separation and transport. Additionally, the introduction of cyano groups extended light absorption range and improved the material’s selective adsorption of reactant molecules. The as-prepared sulfur-modified CN photocatalyst obtained after a 6 h thermal treatment, which was capable of degrading organic pollutants and producing hydrogen (H₂) efficiently and stably, exhibited excellent catalytic performance. The photocatalyst’s photocatalyst exhibited a significantly enhanced photocatalytic activity, with a Rhodamine B (RhB) removal efficiency reaching 97.3%. Meanwhile, the H₂ production level reached 1221.47 μmol h⁻¹g⁻¹. Based on four-cycle experiments, the photocatalyst exhibited excellent recyclability and stability in both H₂ production processes and photocatalytic organic pollutant degradation. In addition, mechanistic studies confirmed the dominant role of ·OH and ·O₂⁻ as active species responsible for the reaction system’s performance. This study highlights that the co-decoration of heteroatoms and functional groups can markedly enhance the photocatalytic performance of CN-based materials, offering considerable potential for future applications in energy conversion and environmental remediation. Full article

Structured illumination microscopy (SIM) with π/2 phase-shift modulation traditionally relies on frequency-domain computation, which greatly limits processing efficiency. In addition, the illumination regime inherent in structured illumination techniques often results in poor visual quality of reconstructed images. To address these dual challenges, this study introduces DM-SIM-LLIE (Differential Low-Light Image Enhancement SIM), a novel framework that integrates two synergistic innovations. First, the study pioneers a spatial-domain computational paradigm for π/2 phase-shift SIM reconstruction. Through system differentiation, mathematical derivation, and algorithm simplification, an optimized spatial-domain model is established. Second, an adaptive local overexposure correction strategy is developed, combined with a zero-shot learning deep learning algorithm, RUAS, to enhance the image quality of structured light reconstructed images. Experimental validation using specimens such as fluorescent microspheres and bovine pulmonary artery endothelial cells demonstrates the advantages of this approach: compared with traditional frequency-domain methods, the reconstruction speed is accelerated by five times while maintaining equivalent lateral resolution and excellent axial resolution. The image quality of the low-light enhancement algorithm after local overexposure correction is superior to existing methods. These advances significantly increase the application potential of SIM technology in time-sensitive biomedical imaging scenarios that require high spatiotemporal resolution. Full article

Biofilms are communities of microorganisms that pose a problem in many areas, including the food industry, drinking water treatment, and medicine, because they can contain pathogens and are difficult to eliminate. For this reason, the possibility of biofilm reduction by ultraviolet (UV) or visible light was investigated using data from published reports. Results for different applications, spectral ranges, and microorganisms were compared by performing MANOVA tests. Approximately 140 publications were found that dealt with the irradiation of water or surfaces for biofilm reduction or reduction in biofilm formation. Irradiation of surfaces with UV or visible light in the spectral range 200–525 nm had a positive effect on biofilm reduction and reduction in biofilm formation, although the results for irradiation of water were conflicting. Most investigations were carried out on P. aeruginosa biofilms, but other Gram-positive and Gram-negative bacteria, as well as some fungi and their biofilm sensitivities to irradiation, were also analyzed. Limited data were available for the UVB (280–315 nm) and UVA (315–400 nm) range. Most experiments to date have been carried out in the UVC (100–280 nm) or in the visible violet/blue spectral (400–500 nm) range, with the UVC range being 2–3 orders of magnitude more efficient in terms of applied irradiation dose. Other quantitative statements were difficult to make as the results from the different working groups were highly scattered. Irradiation can reduce the microorganisms in biofilms but does not completely remove biofilms. New biofilm formation can at least be delayed by surface irradiation. Whether it is also possible to prevent the formation of new biofilms in the long term is open to question. Which irradiation wavelengths are optimal for anti-biofilm measures is also still unclear. Full article

This paper introduces a novel sensor fusion approach for the detection of multiple static and mobile targets by autonomous mobile agents. Unlike previous studies that rely on theoretical sensor models, which are considered as independent, the proposed methodology leverages real-world sensor data, which is transformed into sensor-specific probability maps using object detection estimation for optical data and converting averaged point-cloud intensities for LIDAR based on a dedicated deep learning model before being integrated through a large language model (LLM) framework. We introduce a methodology based on LLM transfer learning (LLM-TLFT) to create a robust global probability map enabling efficient swarm management and target detection in challenging environments. The paper focuses on real data obtained from two types of sensors, light detection and ranging (LIDAR) sensors and optical sensors, and it demonstrates significant improvement in performance compared to existing methods (Independent Opinion Pool, CNN, GPT-2 with deep transfer learning) in terms of precision, recall, and computational efficiency, particularly in scenarios with high noise and sensor imperfections. The significant advantage of the proposed approach is the possibility to interpret a dependency between different sensors. In addition, a model compression using knowledge-based distillation was performed (distilled TLFT), which yielded satisfactory results for the deployment of the proposed approach to edge devices. Full article